---
title: "Racial Segregation and Income Disparities"
output:
flexdashboard::flex_dashboard:
orientation: rows
theme: simplex
source_code: embed
---
```{r setup, include=FALSE}
library(ggplot2)
library(tidyverse)
library(dplyr)
library(gridExtra)
library(plotly)
library(plyr)
library(flexdashboard)
#working directory
setwd("~/Dropbox/HARRIS/Spring 2018/DATA VIZ/Final paper/Flex")
#Read csv
ethnicity_tidy <- read.csv("ethnicity_tidy.csv")
new_data_ethnicity_percent_tidy <- read.csv("new_data_ethnicity_percent_tidy.csv")
race_tidy <- read.csv("race_tidy.csv")
new_data_race_percent_tidy <- read.csv("new_data_race_percent_tidy.csv")
```
Demographics
=======================================================================
Row
-----------------------------------------------------------------------
### Community population by ethnicity
```{r}
dem_1 <- ethnicity_tidy %>%
plot_ly(x = ~place, y = ~ ethnicity_tidy$Population, color = ~Ethnicity) %>%
layout(xaxis = list(
title = ""), margin = list(b = 70),
yaxis = list(title = "Population"))
dem_1
```
### Community population by race
```{r}
dem_3 <- race_tidy %>%
plot_ly(x = ~place, y = ~ race_tidy$Population, color = ~Race) %>%
layout(xaxis = list(
title = ""), margin = list(b = 70),
yaxis = list(title = "Population"))
dem_3
```
Row
-----------------------------------------------------------------------
### Ethnicity distribution within community
```{r}
dem_2 <- new_data_ethnicity_percent_tidy %>%
plot_ly(x = ~Ethnicity, y = ~ new_data_ethnicity_percent_tidy$Percentage, color = ~place )%>%
layout(
yaxis = list(title = "Percentage of people"))
dem_2
```
### Race distribution within community
```{r}
dem_4 <- new_data_race_percent_tidy %>%
plot_ly(x = ~Race, y = ~ new_data_race_percent_tidy$Percent, color = ~place )%>%
layout(
yaxis = list(title = "Percentage of people"))
dem_4
```
Income
=======================================================================
### Percentage of HH with income above the median
```{r}
above_median_HH <- read.csv("above_median_per.csv")
above_median_HH$Race <- factor(above_median_HH$Race,
levels = c('Asian', 'White', 'All', 'Hispanic', 'Other', 'Black'))
inc_1 <- plot_ly(above_median_HH, x = ~Race, y = ~Percentage, type = 'bar',
marker = list(color = 'rgba(222,45,38,0.8)',
line = list(color = I("black"),
width = 1.5))) %>%
layout(
xaxis = list(title = "Race"),
yaxis = list(title = "Percentage of HH"))
inc_1
```
### Percentage of HH by income bracket
```{r}
tidy_income_race_pop <- read.csv("tidy_percent_pop_tot2.csv")
tidy_income_race_pop$Income <- factor(tidy_income_race_pop$Income,
levels = c('$50,000 to $59,999', '$60,000 to $74,999', '$75,000 to $99,999', '$100,000 to $124,999', '$125,000 to $149,999', '$150,000 to $199,999', '$200,000 or more' ))
inc_2 <- plot_ly(tidy_income_race_pop, x = ~Percentage, y = ~Income, color = ~Race) %>%
layout(xaxis = list(title = ''), yaxis = list(title = ''), barmode = 'stack', margin = list(l = 150, r = 0, t = 40, b = 5), legend = list(orientation = "h",
xanchor = "center",
x = 0.5))
inc_2
```
### Percentage of HH by income bracket
```{r}
tidy_income_hisp <- read.csv("tidy_percent_pop_tot2_Hisp.csv")
tidy_income_hisp$Income <- factor(tidy_income_hisp$Income,
levels = c('$50,000 to $59,999', '$60,000 to $74,999', '$75,000 to $99,999', '$100,000 to $124,999', '$125,000 to $149,999', '$150,000 to $199,999', '$200,000 or more' ))
inc_3 <- plot_ly(tidy_income_hisp, x = ~Percentage, y = ~Income, color = ~Race) %>%
layout(xaxis = list(title = ''), yaxis = list(title = ''), barmode = 'stack', margin = list(l = 150, r = 0, t = 40, b = 5), legend = list(orientation = "h",
xanchor = "center",
x = 0.5))
inc_3
```